Designing an Enhanced Swarm-Based Optimization Algorithm for High Utility Itemsets Mining And Its Implementation

DOI

10.2991/978-94-6463-872-1_6How to use a DOI?

Keywords

High Utility Itemset Mining (HUIM); Swarm Intelligence; Particle Swarm Optimization (PSO); Ant Colony Optimization (ACO); Hybrid Optimization Algorithms

Abstract

“High Utility Itemset Mining” (“HUIM”) is a critical data mining technique aimed at identifying item combinations in transactional datasets that generate significant utility, such as profit or revenue. Traditional frequent itemset mining methods often fail to capture the value dimension, making HUIM essential for applications in retail, healthcare, and e-commerce. However, the computational complexity of HUIM presents challenges in handling large and dense datasets.

This study introduces an Enhanced Swarm-Based Optimization Algorithm tailored for HUIM. The algorithm integrates the adaptive parameter tuning capabilities of “Particle Swarm Optimization” (“PSO”) with the pheromone-inspired mechanisms of Ant Colony Optimization (“ACO”). Key improvements include dynamic parameter adjustments to balance exploration and exploitation and hybridization strategies to overcome issues like premature convergence. The algorithm’s effectiveness is demonstrated on retail datasets, revealing high-utility itemsets that provide actionable insights for inventory management, marketing, and strategic decision-making.

Compared to standard PSO, the enhanced algorithm achieves superior performance, including higher utility discovery, faster convergence, and improved scalability. This research bridges theoretical advancements in swarm intelligence with practical applications, offering a robust framework for mining valuable patterns in complex datasets. Future work aims to extend the algorithm to larger datasets, real-time data streams, and dynamic utility scenarios.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yogesh Juyal
AU  - Sonal Sharma
PY  - 2025
DA  - 2025/11/04
TI  - Designing an Enhanced Swarm-Based Optimization Algorithm for High Utility Itemsets Mining And Its Implementation
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 61
EP  - 74
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_6
DO  - 10.2991/978-94-6463-872-1_6
ID  - Juyal2025
ER  -